Volunteer Summary

CONSORT Flow Diagram

Overall status

Characteristic

Overall1

Control1

Treatment1

time_point

1st

90

50

40

2nd

64

31

33

1n

Demographic information

Characteristic

N

Overall, N = 901

control, N = 501

treatment, N = 401

p-value2

age

90

40.53 ± 17.78 (21 - 148)

41.46 ± 19.78 (22 - 148)

39.36 ± 15.09 (21 - 70)

0.580

gender

90

0.232

female

64 (71%)

33 (66%)

31 (78%)

male

26 (29%)

17 (34%)

9 (22%)

occupation

90

0.754

civil

3 (3.3%)

2 (4.0%)

1 (2.5%)

clerk

17 (19%)

8 (16%)

9 (22%)

homemaker

8 (8.9%)

3 (6.0%)

5 (12%)

manager

11 (12%)

7 (14%)

4 (10%)

other

10 (11%)

4 (8.0%)

6 (15%)

professional

13 (14%)

10 (20%)

3 (7.5%)

retired

4 (4.4%)

2 (4.0%)

2 (5.0%)

service

4 (4.4%)

2 (4.0%)

2 (5.0%)

student

18 (20%)

11 (22%)

7 (18%)

unemploy

2 (2.2%)

1 (2.0%)

1 (2.5%)

working_status

90

58 (64%)

33 (66%)

25 (62%)

0.730

marital

90

0.715

divorced

3 (3.3%)

1 (2.0%)

2 (5.0%)

married

25 (28%)

15 (30%)

10 (25%)

single

61 (68%)

33 (66%)

28 (70%)

widowed

1 (1.1%)

1 (2.0%)

0 (0%)

marital_r

90

0.866

married

25 (28%)

15 (30%)

10 (25%)

other

4 (4.4%)

2 (4.0%)

2 (5.0%)

single

61 (68%)

33 (66%)

28 (70%)

education

90

0.017

primary

0 (0%)

0 (0%)

0 (0%)

secondary

11 (12%)

2 (4.0%)

9 (22%)

post-secondary

15 (17%)

11 (22%)

4 (10%)

university

64 (71%)

37 (74%)

27 (68%)

university_edu

90

64 (71%)

37 (74%)

27 (68%)

0.499

family_income

90

0.335

0_10000

11 (12%)

5 (10%)

6 (15%)

10001_20000

19 (21%)

7 (14%)

12 (30%)

20001_30000

14 (16%)

9 (18%)

5 (12%)

30001_40000

13 (14%)

8 (16%)

5 (12%)

40000_above

33 (37%)

21 (42%)

12 (30%)

high_income

90

46 (51%)

29 (58%)

17 (42%)

0.144

religion

90

0.567

buddhism

5 (5.6%)

4 (8.0%)

1 (2.5%)

catholic

5 (5.6%)

2 (4.0%)

3 (7.5%)

christianity

33 (37%)

19 (38%)

14 (35%)

nil

45 (50%)

25 (50%)

20 (50%)

other

1 (1.1%)

0 (0%)

1 (2.5%)

taoism

1 (1.1%)

0 (0%)

1 (2.5%)

religion_r

90

>0.999

christianity

38 (42%)

21 (42%)

17 (42%)

nil

45 (50%)

25 (50%)

20 (50%)

other

7 (7.8%)

4 (8.0%)

3 (7.5%)

source

90

0.023

bokss

38 (42%)

17 (34%)

21 (52%)

facebook

12 (13%)

10 (20%)

2 (5.0%)

instagram

6 (6.7%)

6 (12%)

0 (0%)

other

17 (19%)

8 (16%)

9 (22%)

refresh

17 (19%)

9 (18%)

8 (20%)

1Mean ± SD (Range); n (%)

2Two Sample t-test; Pearson's Chi-squared test; Fisher's exact test

Measurement

Characteristic

N

Overall, N = 901

control, N = 501

treatment, N = 401

p-value2

sets

90

19.41 ± 2.28 (15 - 25)

19.04 ± 2.13 (15 - 24)

19.88 ± 2.40 (15 - 25)

0.084

setv

90

11.23 ± 1.68 (8 - 15)

11.08 ± 1.63 (8 - 15)

11.43 ± 1.75 (8 - 15)

0.337

maks

90

44.86 ± 3.84 (36 - 57)

44.38 ± 3.56 (36 - 52)

45.45 ± 4.14 (38 - 57)

0.191

ibs

90

15.67 ± 2.15 (9 - 20)

15.64 ± 2.03 (11 - 20)

15.70 ± 2.31 (9 - 20)

0.896

ers_e

90

12.24 ± 1.46 (8 - 15)

12.22 ± 1.52 (8 - 15)

12.28 ± 1.40 (9 - 15)

0.860

ers_r

90

11.32 ± 1.50 (8 - 15)

11.20 ± 1.39 (8 - 14)

11.47 ± 1.63 (8 - 15)

0.390

pss_pa

90

45.07 ± 4.57 (30 - 54)

44.70 ± 4.44 (30 - 54)

45.52 ± 4.74 (31 - 54)

0.398

pss_ps

90

25.49 ± 7.23 (12 - 42)

26.28 ± 7.39 (13 - 42)

24.50 ± 7.00 (12 - 41)

0.248

pss

90

43.42 ± 11.05 (21 - 72)

44.58 ± 11.20 (22 - 72)

41.98 ± 10.83 (21 - 67)

0.269

rki_responsible

90

21.23 ± 4.02 (13 - 29)

20.86 ± 4.32 (13 - 29)

21.70 ± 3.60 (14 - 28)

0.327

rki_nonlinear

90

13.40 ± 2.73 (6 - 22)

13.18 ± 2.56 (6 - 20)

13.68 ± 2.95 (8 - 22)

0.397

rki_peer

90

20.43 ± 2.18 (16 - 25)

20.48 ± 2.18 (16 - 25)

20.38 ± 2.20 (16 - 25)

0.822

rki_expect

90

4.68 ± 1.07 (2 - 8)

4.48 ± 1.09 (2 - 8)

4.92 ± 1.00 (3 - 7)

0.049

rki

90

59.74 ± 5.86 (45 - 80)

59.00 ± 5.99 (45 - 76)

60.67 ± 5.64 (50 - 80)

0.179

raq_possible

90

15.58 ± 1.82 (12 - 20)

15.62 ± 1.90 (12 - 20)

15.53 ± 1.74 (12 - 20)

0.808

raq_difficulty

90

12.33 ± 1.41 (9 - 15)

12.46 ± 1.42 (9 - 15)

12.18 ± 1.41 (9 - 15)

0.345

raq

90

27.91 ± 2.96 (21 - 35)

28.08 ± 3.08 (21 - 35)

27.70 ± 2.84 (21 - 35)

0.548

who

90

15.04 ± 4.35 (6 - 25)

15.06 ± 4.19 (8 - 25)

15.03 ± 4.59 (6 - 25)

0.970

phq

90

3.40 ± 3.64 (0 - 18)

3.36 ± 3.44 (0 - 14)

3.45 ± 3.92 (0 - 18)

0.908

gad

90

2.90 ± 3.02 (0 - 12)

2.96 ± 2.97 (0 - 12)

2.83 ± 3.11 (0 - 12)

0.834

nb_pcs

90

51.16 ± 7.74 (25 - 63)

51.92 ± 7.51 (25 - 63)

50.20 ± 8.01 (27 - 61)

0.297

nb_mcs

90

51.14 ± 8.25 (22 - 70)

50.86 ± 8.31 (22 - 68)

51.48 ± 8.27 (35 - 70)

0.724

1Mean ± SD (Range)

2Two Sample t-test

Data analysis

Table

Group

Characteristic

Beta

SE1

95% CI1

p-value

sets

(Intercept)

19.0

0.305

18.4, 19.6

group

control

—

—

—

treatment

0.835

0.457

-0.061, 1.73

0.070

time_point

1st

—

—

—

2nd

-0.302

0.397

-1.08, 0.476

0.449

group * time_point

treatment * 2nd

0.220

0.562

-0.881, 1.32

0.696

Pseudo R square

0.046

setv

(Intercept)

11.1

0.240

10.6, 11.6

group

control

—

—

—

treatment

0.345

0.360

-0.361, 1.05

0.340

time_point

1st

—

—

—

2nd

0.195

0.268

-0.330, 0.719

0.469

group * time_point

treatment * 2nd

-0.051

0.377

-0.790, 0.687

0.892

Pseudo R square

0.012

maks

(Intercept)

44.4

0.556

43.3, 45.5

group

control

—

—

—

treatment

1.07

0.834

-0.564, 2.70

0.202

time_point

1st

—

—

—

2nd

-0.095

0.532

-1.14, 0.948

0.859

group * time_point

treatment * 2nd

0.029

0.747

-1.43, 1.49

0.969

Pseudo R square

0.019

ibs

(Intercept)

15.6

0.297

15.1, 16.2

group

control

—

—

—

treatment

0.060

0.445

-0.813, 0.933

0.893

time_point

1st

—

—

—

2nd

0.136

0.312

-0.476, 0.748

0.664

group * time_point

treatment * 2nd

0.443

0.439

-0.417, 1.30

0.316

Pseudo R square

0.013

ers_e

(Intercept)

12.2

0.206

11.8, 12.6

group

control

—

—

—

treatment

0.055

0.308

-0.549, 0.659

0.859

time_point

1st

—

—

—

2nd

-0.535

0.208

-0.944, -0.127

0.012

group * time_point

treatment * 2nd

0.726

0.293

0.152, 1.30

0.016

Pseudo R square

0.032

ers_r

(Intercept)

11.2

0.202

10.8, 11.6

group

control

—

—

—

treatment

0.275

0.302

-0.318, 0.868

0.365

time_point

1st

—

—

—

2nd

-0.152

0.261

-0.663, 0.359

0.563

group * time_point

treatment * 2nd

0.218

0.369

-0.505, 0.941

0.557

Pseudo R square

0.018

pss_pa

(Intercept)

44.7

0.647

43.4, 46.0

group

control

—

—

—

treatment

0.825

0.971

-1.08, 2.73

0.397

time_point

1st

—

—

—

2nd

-1.55

0.807

-3.14, 0.027

0.058

group * time_point

treatment * 2nd

0.561

1.141

-1.67, 2.80

0.624

Pseudo R square

0.030

pss_ps

(Intercept)

26.3

1.026

24.3, 28.3

group

control

—

—

—

treatment

-1.78

1.538

-4.80, 1.24

0.250

time_point

1st

—

—

—

2nd

1.49

1.165

-0.796, 3.77

0.206

group * time_point

treatment * 2nd

-1.50

1.641

-4.71, 1.72

0.365

Pseudo R square

0.031

pss

(Intercept)

44.6

1.541

41.6, 47.6

group

control

—

—

—

treatment

-2.60

2.312

-7.14, 1.93

0.262

time_point

1st

—

—

—

2nd

2.98

1.689

-0.327, 6.29

0.081

group * time_point

treatment * 2nd

-1.93

2.378

-6.60, 2.73

0.419

Pseudo R square

0.032

rki_responsible

(Intercept)

20.9

0.560

19.8, 22.0

group

control

—

—

—

treatment

0.840

0.841

-0.808, 2.49

0.320

time_point

1st

—

—

—

2nd

-0.037

0.621

-1.25, 1.18

0.953

group * time_point

treatment * 2nd

-0.433

0.874

-2.15, 1.28

0.622

Pseudo R square

0.008

rki_nonlinear

(Intercept)

13.2

0.418

12.4, 14.0

group

control

—

—

—

treatment

0.495

0.628

-0.735, 1.73

0.432

time_point

1st

—

—

—

2nd

-0.270

0.470

-1.19, 0.652

0.567

group * time_point

treatment * 2nd

0.462

0.663

-0.836, 1.76

0.488

Pseudo R square

0.015

rki_peer

(Intercept)

20.5

0.320

19.9, 21.1

group

control

—

—

—

treatment

-0.105

0.481

-1.05, 0.837

0.827

time_point

1st

—

—

—

2nd

0.100

0.380

-0.646, 0.845

0.794

group * time_point

treatment * 2nd

0.121

0.537

-0.930, 1.17

0.822

Pseudo R square

0.001

rki_expect

(Intercept)

4.48

0.142

4.20, 4.76

group

control

—

—

—

treatment

0.445

0.212

0.029, 0.861

0.038

time_point

1st

—

—

—

2nd

0.086

0.199

-0.305, 0.477

0.667

group * time_point

treatment * 2nd

0.102

0.283

-0.453, 0.657

0.719

Pseudo R square

0.063

rki

(Intercept)

59.0

0.835

57.4, 60.6

group

control

—

—

—

treatment

1.67

1.253

-0.781, 4.13

0.184

time_point

1st

—

—

—

2nd

-0.097

0.939

-1.94, 1.74

0.918

group * time_point

treatment * 2nd

0.246

1.322

-2.35, 2.84

0.853

Pseudo R square

0.022

raq_possible

(Intercept)

15.6

0.252

15.1, 16.1

group

control

—

—

—

treatment

-0.095

0.379

-0.837, 0.647

0.802

time_point

1st

—

—

—

2nd

-0.265

0.298

-0.848, 0.319

0.377

group * time_point

treatment * 2nd

0.682

0.420

-0.140, 1.50

0.108

Pseudo R square

0.012

raq_difficulty

(Intercept)

12.5

0.199

12.1, 12.8

group

control

—

—

—

treatment

-0.285

0.298

-0.870, 0.300

0.341

time_point

1st

—

—

—

2nd

-0.076

0.234

-0.534, 0.382

0.746

group * time_point

treatment * 2nd

0.277

0.330

-0.369, 0.923

0.403

Pseudo R square

0.006

raq

(Intercept)

28.1

0.417

27.3, 28.9

group

control

—

—

—

treatment

-0.380

0.625

-1.61, 0.845

0.544

time_point

1st

—

—

—

2nd

-0.292

0.461

-1.19, 0.611

0.528

group * time_point

treatment * 2nd

0.909

0.648

-0.362, 2.18

0.165

Pseudo R square

0.006

who

(Intercept)

15.1

0.613

13.9, 16.3

group

control

—

—

—

treatment

-0.035

0.919

-1.84, 1.77

0.970

time_point

1st

—

—

—

2nd

-0.293

0.623

-1.51, 0.929

0.640

group * time_point

treatment * 2nd

0.851

0.876

-0.866, 2.57

0.335

Pseudo R square

0.004

phq

(Intercept)

3.36

0.492

2.40, 4.32

group

control

—

—

—

treatment

0.090

0.738

-1.36, 1.54

0.903

time_point

1st

—

—

—

2nd

0.203

0.399

-0.579, 0.985

0.613

group * time_point

treatment * 2nd

-0.171

0.559

-1.27, 0.924

0.761

Pseudo R square

0.000

gad

(Intercept)

2.96

0.437

2.10, 3.82

group

control

—

—

—

treatment

-0.135

0.656

-1.42, 1.15

0.837

time_point

1st

—

—

—

2nd

0.299

0.443

-0.569, 1.17

0.501

group * time_point

treatment * 2nd

-0.151

0.622

-1.37, 1.07

0.809

Pseudo R square

0.002

nb_pcs

(Intercept)

51.9

1.046

49.9, 54.0

group

control

—

—

—

treatment

-1.72

1.569

-4.80, 1.35

0.275

time_point

1st

—

—

—

2nd

-0.994

0.946

-2.85, 0.859

0.297

group * time_point

treatment * 2nd

1.98

1.326

-0.618, 4.58

0.140

Pseudo R square

0.008

nb_mcs

(Intercept)

50.9

1.145

48.6, 53.1

group

control

—

—

—

treatment

0.622

1.718

-2.75, 3.99

0.718

time_point

1st

—

—

—

2nd

-0.325

1.282

-2.84, 2.19

0.800

group * time_point

treatment * 2nd

0.247

1.806

-3.29, 3.79

0.891

Pseudo R square

0.002

1SE = Standard Error, CI = Confidence Interval

Text

sets

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict sets with group and time_point (formula: sets ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.43) and the part related to the fixed effects alone (marginal R2) is of 0.05. The model’s intercept, corresponding to group = control and time_point = 1st, is at 19.04 (95% CI [18.44, 19.64], t(148) = 62.48, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.83, 95% CI [-0.06, 1.73], t(148) = 1.83, p = 0.068; Std. beta = 0.38, 95% CI [-0.03, 0.79])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.30, 95% CI [-1.08, 0.48], t(148) = -0.76, p = 0.447; Std. beta = -0.14, 95% CI [-0.49, 0.22])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.22, 95% CI [-0.88, 1.32], t(148) = 0.39, p = 0.695; Std. beta = 0.10, 95% CI [-0.40, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

setv

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict setv with group and time_point (formula: setv ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.08 (95% CI [10.61, 11.55], t(148) = 46.16, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.34, 95% CI [-0.36, 1.05], t(148) = 0.96, p = 0.338; Std. beta = 0.20, 95% CI [-0.21, 0.62])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.19, 95% CI [-0.33, 0.72], t(148) = 0.73, p = 0.467; Std. beta = 0.11, 95% CI [-0.19, 0.42])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.05, 95% CI [-0.79, 0.69], t(148) = -0.14, p = 0.892; Std. beta = -0.03, 95% CI [-0.46, 0.40])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

maks

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict maks with group and time_point (formula: maks ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.70) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.38 (95% CI [43.29, 45.47], t(148) = 79.87, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 1.07, 95% CI [-0.56, 2.70], t(148) = 1.28, p = 0.199; Std. beta = 0.27, 95% CI [-0.14, 0.69])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-1.14, 0.95], t(148) = -0.18, p = 0.858; Std. beta = -0.02, 95% CI [-0.29, 0.24])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.03, 95% CI [-1.43, 1.49], t(148) = 0.04, p = 0.969; Std. beta = 7.44e-03, 95% CI [-0.37, 0.38])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ibs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ibs with group and time_point (formula: ibs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.64) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.64 (95% CI [15.06, 16.22], t(148) = 52.68, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.06, 95% CI [-0.81, 0.93], t(148) = 0.13, p = 0.893; Std. beta = 0.03, 95% CI [-0.38, 0.44])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.14, 95% CI [-0.48, 0.75], t(148) = 0.44, p = 0.663; Std. beta = 0.06, 95% CI [-0.23, 0.35])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.44, 95% CI [-0.42, 1.30], t(148) = 1.01, p = 0.313; Std. beta = 0.21, 95% CI [-0.20, 0.62])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_e

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_e with group and time_point (formula: ers_e ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.67) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.22 (95% CI [11.82, 12.62], t(148) = 59.43, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.06, 95% CI [-0.55, 0.66], t(148) = 0.18, p = 0.858; Std. beta = 0.04, 95% CI [-0.38, 0.45])
  • The effect of time point [2nd] is statistically significant and negative (beta = -0.54, 95% CI [-0.94, -0.13], t(148) = -2.57, p = 0.010; Std. beta = -0.37, 95% CI [-0.65, -0.09])
  • The interaction effect of time point [2nd] on group [treatment] is statistically significant and positive (beta = 0.73, 95% CI [0.15, 1.30], t(148) = 2.48, p = 0.013; Std. beta = 0.50, 95% CI [0.10, 0.89])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

ers_r

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict ers_r with group and time_point (formula: ers_r ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.43) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 11.20 (95% CI [10.80, 11.60], t(148) = 55.55, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.27, 95% CI [-0.32, 0.87], t(148) = 0.91, p = 0.363; Std. beta = 0.19, 95% CI [-0.22, 0.61])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.15, 95% CI [-0.66, 0.36], t(148) = -0.58, p = 0.561; Std. beta = -0.11, 95% CI [-0.46, 0.25])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.22, 95% CI [-0.51, 0.94], t(148) = 0.59, p = 0.555; Std. beta = 0.15, 95% CI [-0.35, 0.66])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_pa

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_pa with group and time_point (formula: pss_pa ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.48) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.70 (95% CI [43.43, 45.97], t(148) = 69.04, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.82, 95% CI [-1.08, 2.73], t(148) = 0.85, p = 0.396; Std. beta = 0.18, 95% CI [-0.23, 0.59])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -1.55, 95% CI [-3.14, 0.03], t(148) = -1.93, p = 0.054; Std. beta = -0.34, 95% CI [-0.68, 5.86e-03])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.56, 95% CI [-1.67, 2.80], t(148) = 0.49, p = 0.623; Std. beta = 0.12, 95% CI [-0.36, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss_ps

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss_ps with group and time_point (formula: pss_ps ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 26.28 (95% CI [24.27, 28.29], t(148) = 25.62, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -1.78, 95% CI [-4.80, 1.24], t(148) = -1.16, p = 0.247; Std. beta = -0.24, 95% CI [-0.65, 0.17])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 1.49, 95% CI [-0.80, 3.77], t(148) = 1.28, p = 0.202; Std. beta = 0.20, 95% CI [-0.11, 0.51])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.50, 95% CI [-4.71, 1.72], t(148) = -0.91, p = 0.362; Std. beta = -0.20, 95% CI [-0.64, 0.23])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

pss

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict pss with group and time_point (formula: pss ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.61) and the part related to the fixed effects alone (marginal R2) is of 0.03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 44.58 (95% CI [41.56, 47.60], t(148) = 28.93, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -2.60, 95% CI [-7.14, 1.93], t(148) = -1.13, p = 0.260; Std. beta = -0.24, 95% CI [-0.64, 0.17])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 2.98, 95% CI [-0.33, 6.29], t(148) = 1.77, p = 0.077; Std. beta = 0.27, 95% CI [-0.03, 0.57])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -1.93, 95% CI [-6.60, 2.73], t(148) = -0.81, p = 0.416; Std. beta = -0.17, 95% CI [-0.60, 0.25])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_responsible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_responsible with group and time_point (formula: rki_responsible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 8.18e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.86 (95% CI [19.76, 21.96], t(148) = 37.22, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.84, 95% CI [-0.81, 2.49], t(148) = 1.00, p = 0.318; Std. beta = 0.22, 95% CI [-0.21, 0.64])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.04, 95% CI [-1.25, 1.18], t(148) = -0.06, p = 0.953; Std. beta = -9.47e-03, 95% CI [-0.32, 0.30])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.43, 95% CI [-2.15, 1.28], t(148) = -0.50, p = 0.620; Std. beta = -0.11, 95% CI [-0.55, 0.33])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_nonlinear

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_nonlinear with group and time_point (formula: rki_nonlinear ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 13.18 (95% CI [12.36, 14.00], t(148) = 31.50, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.49, 95% CI [-0.74, 1.73], t(148) = 0.79, p = 0.430; Std. beta = 0.17, 95% CI [-0.25, 0.59])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.27, 95% CI [-1.19, 0.65], t(148) = -0.57, p = 0.566; Std. beta = -0.09, 95% CI [-0.41, 0.22])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.46, 95% CI [-0.84, 1.76], t(148) = 0.70, p = 0.485; Std. beta = 0.16, 95% CI [-0.29, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_peer

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_peer with group and time_point (formula: rki_peer ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.52) and the part related to the fixed effects alone (marginal R2) is of 1.46e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 20.48 (95% CI [19.85, 21.11], t(148) = 63.92, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.11, 95% CI [-1.05, 0.84], t(148) = -0.22, p = 0.827; Std. beta = -0.05, 95% CI [-0.47, 0.37])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.65, 0.85], t(148) = 0.26, p = 0.793; Std. beta = 0.04, 95% CI [-0.29, 0.38])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.12, 95% CI [-0.93, 1.17], t(148) = 0.23, p = 0.821; Std. beta = 0.05, 95% CI [-0.41, 0.52])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki_expect

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki_expect with group and time_point (formula: rki_expect ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.33) and the part related to the fixed effects alone (marginal R2) is of 0.06. The model’s intercept, corresponding to group = control and time_point = 1st, is at 4.48 (95% CI [4.20, 4.76], t(148) = 31.63, p < .001). Within this model:

  • The effect of group [treatment] is statistically significant and positive (beta = 0.44, 95% CI [0.03, 0.86], t(148) = 2.09, p = 0.036; Std. beta = 0.44, 95% CI [0.03, 0.84])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.09, 95% CI [-0.30, 0.48], t(148) = 0.43, p = 0.666; Std. beta = 0.08, 95% CI [-0.30, 0.47])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.10, 95% CI [-0.45, 0.66], t(148) = 0.36, p = 0.718; Std. beta = 0.10, 95% CI [-0.44, 0.64])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

rki

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict rki with group and time_point (formula: rki ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 0.02. The model’s intercept, corresponding to group = control and time_point = 1st, is at 59.00 (95% CI [57.36, 60.64], t(148) = 70.62, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 1.67, 95% CI [-0.78, 4.13], t(148) = 1.34, p = 0.181; Std. beta = 0.29, 95% CI [-0.13, 0.71])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.10, 95% CI [-1.94, 1.74], t(148) = -0.10, p = 0.918; Std. beta = -0.02, 95% CI [-0.33, 0.30])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.25, 95% CI [-2.35, 2.84], t(148) = 0.19, p = 0.852; Std. beta = 0.04, 95% CI [-0.40, 0.49])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_possible

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_possible with group and time_point (formula: raq_possible ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 0.01. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.62 (95% CI [15.13, 16.11], t(148) = 61.87, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.10, 95% CI [-0.84, 0.65], t(148) = -0.25, p = 0.802; Std. beta = -0.05, 95% CI [-0.47, 0.36])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.26, 95% CI [-0.85, 0.32], t(148) = -0.89, p = 0.374; Std. beta = -0.15, 95% CI [-0.47, 0.18])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.68, 95% CI [-0.14, 1.50], t(148) = 1.63, p = 0.104; Std. beta = 0.38, 95% CI [-0.08, 0.84])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq_difficulty

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq_difficulty with group and time_point (formula: raq_difficulty ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.53) and the part related to the fixed effects alone (marginal R2) is of 6.20e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 12.46 (95% CI [12.07, 12.85], t(148) = 62.66, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.29, 95% CI [-0.87, 0.30], t(148) = -0.96, p = 0.339; Std. beta = -0.20, 95% CI [-0.62, 0.21])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.08, 95% CI [-0.53, 0.38], t(148) = -0.32, p = 0.745; Std. beta = -0.05, 95% CI [-0.38, 0.27])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.28, 95% CI [-0.37, 0.92], t(148) = 0.84, p = 0.401; Std. beta = 0.20, 95% CI [-0.26, 0.66])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

raq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict raq with group and time_point (formula: raq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.59) and the part related to the fixed effects alone (marginal R2) is of 6.37e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 28.08 (95% CI [27.26, 28.90], t(148) = 67.38, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.38, 95% CI [-1.61, 0.85], t(148) = -0.61, p = 0.543; Std. beta = -0.13, 95% CI [-0.55, 0.29])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.29, 95% CI [-1.19, 0.61], t(148) = -0.63, p = 0.526; Std. beta = -0.10, 95% CI [-0.41, 0.21])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.91, 95% CI [-0.36, 2.18], t(148) = 1.40, p = 0.161; Std. beta = 0.31, 95% CI [-0.12, 0.74])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

who

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict who with group and time_point (formula: who ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 3.97e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 15.06 (95% CI [13.86, 16.26], t(148) = 24.58, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.04, 95% CI [-1.84, 1.77], t(148) = -0.04, p = 0.970; Std. beta = -8.13e-03, 95% CI [-0.43, 0.41])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.29, 95% CI [-1.51, 0.93], t(148) = -0.47, p = 0.639; Std. beta = -0.07, 95% CI [-0.35, 0.22])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.85, 95% CI [-0.87, 2.57], t(148) = 0.97, p = 0.331; Std. beta = 0.20, 95% CI [-0.20, 0.60])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

phq

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict phq with group and time_point (formula: phq ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.79) and the part related to the fixed effects alone (marginal R2) is of 4.50e-04. The model’s intercept, corresponding to group = control and time_point = 1st, is at 3.36 (95% CI [2.40, 4.32], t(148) = 6.83, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.09, 95% CI [-1.36, 1.54], t(148) = 0.12, p = 0.903; Std. beta = 0.03, 95% CI [-0.39, 0.44])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.20, 95% CI [-0.58, 0.98], t(148) = 0.51, p = 0.611; Std. beta = 0.06, 95% CI [-0.17, 0.28])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.17, 95% CI [-1.27, 0.92], t(148) = -0.31, p = 0.760; Std. beta = -0.05, 95% CI [-0.36, 0.27])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

gad

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict gad with group and time_point (formula: gad ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.66) and the part related to the fixed effects alone (marginal R2) is of 2.32e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 2.96 (95% CI [2.10, 3.82], t(148) = 6.77, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -0.13, 95% CI [-1.42, 1.15], t(148) = -0.21, p = 0.837; Std. beta = -0.04, 95% CI [-0.46, 0.37])
  • The effect of time point [2nd] is statistically non-significant and positive (beta = 0.30, 95% CI [-0.57, 1.17], t(148) = 0.68, p = 0.499; Std. beta = 0.10, 95% CI [-0.18, 0.38])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and negative (beta = -0.15, 95% CI [-1.37, 1.07], t(148) = -0.24, p = 0.808; Std. beta = -0.05, 95% CI [-0.44, 0.34])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_pcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_pcs with group and time_point (formula: nb_pcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.74) and the part related to the fixed effects alone (marginal R2) is of 7.98e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 51.92 (95% CI [49.87, 53.97], t(148) = 49.65, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and negative (beta = -1.72, 95% CI [-4.80, 1.35], t(148) = -1.10, p = 0.272; Std. beta = -0.23, 95% CI [-0.65, 0.18])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.99, 95% CI [-2.85, 0.86], t(148) = -1.05, p = 0.293; Std. beta = -0.13, 95% CI [-0.38, 0.12])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 1.98, 95% CI [-0.62, 4.58], t(148) = 1.49, p = 0.135; Std. beta = 0.27, 95% CI [-0.08, 0.62])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

nb_mcs

We fitted a linear mixed model (estimated using REML and nloptwrap optimizer) to predict nb_mcs with group and time_point (formula: nb_mcs ~ 1 + group + time_point + group * time_point). The model included login_id as random effect (formula: ~1 | login_id). The model’s total explanatory power is substantial (conditional R2 = 0.58) and the part related to the fixed effects alone (marginal R2) is of 2.14e-03. The model’s intercept, corresponding to group = control and time_point = 1st, is at 50.86 (95% CI [48.62, 53.11], t(148) = 44.40, p < .001). Within this model:

  • The effect of group [treatment] is statistically non-significant and positive (beta = 0.62, 95% CI [-2.75, 3.99], t(148) = 0.36, p = 0.717; Std. beta = 0.08, 95% CI [-0.34, 0.50])
  • The effect of time point [2nd] is statistically non-significant and negative (beta = -0.33, 95% CI [-2.84, 2.19], t(148) = -0.25, p = 0.800; Std. beta = -0.04, 95% CI [-0.35, 0.27])
  • The interaction effect of time point [2nd] on group [treatment] is statistically non-significant and positive (beta = 0.25, 95% CI [-3.29, 3.79], t(148) = 0.14, p = 0.891; Std. beta = 0.03, 95% CI [-0.41, 0.47])

Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald normal distribution approximation.

Likelihood ratio tests

outcome

model

npar

AIC

BIC

logLik

deviance

Chisq

Df

p

sets

null

3

669.586

678.697

-331.793

663.586

sets

random

6

669.833

688.055

-328.917

657.833

5.752

3

0.124

setv

null

3

577.445

586.556

-285.723

571.445

setv

random

6

581.516

599.738

-284.758

569.516

1.929

3

0.587

maks

null

3

819.550

828.661

-406.775

813.550

maks

random

6

823.604

841.826

-405.802

811.604

1.946

3

0.584

ibs

null

3

638.953

648.064

-316.476

632.953

ibs

random

6

640.855

659.077

-314.427

628.855

4.098

3

0.251

ers_e

null

3

526.164

535.275

-260.082

520.164

ers_e

random

6

523.690

541.912

-255.845

511.690

8.474

3

0.037

ers_r

null

3

538.227

547.338

-266.114

532.227

ers_r

random

6

541.985

560.207

-264.993

529.985

2.242

3

0.524

pss_pa

null

3

898.484

907.595

-446.242

892.484

pss_pa

random

6

898.073

916.295

-443.037

886.073

6.411

3

0.093

pss_ps

null

3

1,029.042

1,038.153

-511.521

1,023.042

pss_ps

random

6

1,030.724

1,048.946

-509.362

1,018.724

4.318

3

0.229

pss

null

3

1,152.243

1,161.354

-573.121

1,146.243

pss

random

6

1,152.475

1,170.696

-570.237

1,140.475

5.768

3

0.123

rki_responsible

null

3

837.367

846.477

-415.683

831.367

rki_responsible

random

6

842.036

860.258

-415.018

830.036

1.331

3

0.722

rki_nonlinear

null

3

749.435

758.546

-371.717

743.435

rki_nonlinear

random

6

753.539

771.761

-370.769

741.539

1.896

3

0.594

rki_peer

null

3

671.192

680.303

-332.596

665.192

rki_peer

random

6

676.760

694.982

-332.380

664.760

0.432

3

0.934

rki_expect

null

3

442.445

451.556

-218.222

436.445

rki_expect

random

6

439.809

458.031

-213.905

427.809

8.636

3

0.035

rki

null

3

962.940

972.050

-478.470

956.940

rki

random

6

966.476

984.697

-477.238

954.476

2.464

3

0.482

raq_possible

null

3

599.750

608.860

-296.875

593.750

raq_possible

random

6

602.655

620.876

-295.327

590.655

3.095

3

0.377

raq_difficulty

null

3

524.143

533.254

-259.072

518.143

raq_difficulty

random

6

528.850

547.072

-258.425

516.850

1.293

3

0.731

raq

null

3

746.814

755.924

-370.407

740.814

raq

random

6

750.539

768.761

-369.270

738.539

2.274

3

0.517

who

null

3

855.604

864.715

-424.802

849.604

who

random

6

860.410

878.632

-424.205

848.410

1.194

3

0.754

phq

null

3

761.186

770.296

-377.593

755.186

phq

random

6

766.913

785.135

-377.457

754.913

0.272

3

0.965

gad

null

3

750.607

759.718

-372.304

744.607

gad

random

6

755.949

774.171

-371.975

743.949

0.658

3

0.883

nb_pcs

null

3

1,008.659

1,017.770

-501.329

1,002.659

nb_pcs

random

6

1,011.945

1,030.166

-499.972

999.945

2.714

3

0.438

nb_mcs

null

3

1,057.519

1,066.630

-525.760

1,051.519

nb_mcs

random

6

1,063.248

1,081.470

-525.624

1,051.248

0.271

3

0.965

Post hoc analysis text

Table

outcome

time

control

treatment

between

n

estimate

within es

n

estimate

within es

p

es

sets

1st

50

19.04 ± 2.15

40

19.88 ± 2.15

0.070

-0.503

sets

2nd

31

18.74 ± 2.10

0.182

33

19.79 ± 2.13

0.049

0.048

-0.636

setv

1st

50

11.08 ± 1.70

40

11.42 ± 1.70

0.340

-0.314

setv

2nd

31

11.27 ± 1.59

-0.177

33

11.57 ± 1.65

-0.131

0.469

-0.268

maks

1st

50

44.38 ± 3.93

40

45.45 ± 3.93

0.202

-0.496

maks

2nd

31

44.28 ± 3.55

0.044

33

45.38 ± 3.76

0.031

0.231

-0.510

ibs

1st

50

15.64 ± 2.10

40

15.70 ± 2.10

0.893

-0.047

ibs

2nd

31

15.78 ± 1.94

-0.107

33

16.28 ± 2.03

-0.454

0.312

-0.395

ers_e

1st

50

12.22 ± 1.45

40

12.27 ± 1.45

0.859

-0.065

ers_e

2nd

31

11.68 ± 1.33

0.631

33

12.47 ± 1.40

-0.225

0.024

-0.921

ers_r

1st

50

11.20 ± 1.43

40

11.47 ± 1.43

0.365

-0.253

ers_r

2nd

31

11.05 ± 1.38

0.139

33

11.54 ± 1.41

-0.061

0.160

-0.453

pss_pa

1st

50

44.70 ± 4.58

40

45.52 ± 4.58

0.397

-0.246

pss_pa

2nd

31

43.15 ± 4.40

0.464

33

44.53 ± 4.50

0.296

0.215

-0.413

pss_ps

1st

50

26.28 ± 7.25

40

24.50 ± 7.25

0.250

0.372

pss_ps

2nd

31

27.77 ± 6.82

-0.311

33

24.49 ± 7.06

0.002

0.061

0.684

pss

1st

50

44.58 ± 10.90

40

41.98 ± 10.90

0.262

0.376

pss

2nd

31

47.56 ± 10.16

-0.431

33

43.02 ± 10.56

-0.152

0.082

0.656

rki_responsible

1st

50

20.86 ± 3.96

40

21.70 ± 3.96

0.320

-0.330

rki_responsible

2nd

31

20.82 ± 3.70

0.015

33

21.23 ± 3.85

0.185

0.667

-0.160

rki_nonlinear

1st

50

13.18 ± 2.96

40

13.67 ± 2.96

0.432

-0.256

rki_nonlinear

2nd

31

12.91 ± 2.78

0.140

33

13.87 ± 2.88

-0.099

0.178

-0.496

rki_peer

1st

50

20.48 ± 2.27

40

20.38 ± 2.27

0.827

0.067

rki_peer

2nd

31

20.58 ± 2.15

-0.064

33

20.60 ± 2.21

-0.141

0.976

-0.010

rki_expect

1st

50

4.48 ± 1.00

40

4.92 ± 1.00

0.038

-0.527

rki_expect

2nd

31

4.57 ± 0.99

-0.102

33

5.11 ± 1.00

-0.223

0.029

-0.648

rki

1st

50

59.00 ± 5.91

40

60.68 ± 5.91

0.184

-0.434

rki

2nd

31

58.90 ± 5.54

0.025

33

60.82 ± 5.74

-0.039

0.175

-0.498

raq_possible

1st

50

15.62 ± 1.79

40

15.53 ± 1.79

0.802

0.077

raq_possible

2nd

31

15.36 ± 1.69

0.215

33

15.94 ± 1.74

-0.340

0.174

-0.478

raq_difficulty

1st

50

12.46 ± 1.41

40

12.18 ± 1.41

0.341

0.296

raq_difficulty

2nd

31

12.38 ± 1.33

0.079

33

12.38 ± 1.37

-0.209

0.981

0.008

raq

1st

50

28.08 ± 2.95

40

27.70 ± 2.95

0.544

0.201

raq

2nd

31

27.79 ± 2.75

0.155

33

28.32 ± 2.86

-0.327

0.452

-0.280

who

1st

50

15.06 ± 4.33

40

15.02 ± 4.33

0.970

0.014

who

2nd

31

14.77 ± 3.97

0.115

33

15.58 ± 4.17

-0.220

0.424

-0.322

phq

1st

50

3.36 ± 3.48

40

3.45 ± 3.48

0.903

-0.056

phq

2nd

31

3.56 ± 3.04

-0.126

33

3.48 ± 3.28

-0.020

0.919

0.050

gad

1st

50

2.96 ± 3.09

40

2.83 ± 3.09

0.837

0.075

gad

2nd

31

3.26 ± 2.83

-0.166

33

2.97 ± 2.97

-0.082

0.694

0.159

nb_pcs

1st

50

51.92 ± 7.40

40

50.20 ± 7.40

0.275

0.451

nb_pcs

2nd

31

50.93 ± 6.61

0.260

33

51.19 ± 7.04

-0.258

0.880

-0.068

nb_mcs

1st

50

50.86 ± 8.10

40

51.48 ± 8.10

0.718

-0.118

nb_mcs

2nd

31

50.54 ± 7.59

0.062

33

51.41 ± 7.87

0.015

0.653

-0.165

Between group

sets

1st

t(132.35) = 1.83, p = 0.070, Cohen d = -0.50, 95% CI (-0.07 to 1.74)

2st

t(146.78) = 2.00, p = 0.048, Cohen d = -0.64, 95% CI (0.01 to 2.10)

setv

1st

t(118.65) = 0.96, p = 0.340, Cohen d = -0.31, 95% CI (-0.37 to 1.06)

2st

t(140.58) = 0.73, p = 0.469, Cohen d = -0.27, 95% CI (-0.51 to 1.09)

maks

1st

t(109.33) = 1.28, p = 0.202, Cohen d = -0.50, 95% CI (-0.58 to 2.72)

2st

t(132.43) = 1.20, p = 0.231, Cohen d = -0.51, 95% CI (-0.71 to 2.91)

ibs

1st

t(114.67) = 0.13, p = 0.893, Cohen d = -0.05, 95% CI (-0.82 to 0.94)

2st

t(137.63) = 1.02, p = 0.312, Cohen d = -0.39, 95% CI (-0.48 to 1.48)

ers_e

1st

t(112.40) = 0.18, p = 0.859, Cohen d = -0.06, 95% CI (-0.56 to 0.67)

2st

t(135.62) = 2.29, p = 0.024, Cohen d = -0.92, 95% CI (0.11 to 1.46)

ers_r

1st

t(131.62) = 0.91, p = 0.365, Cohen d = -0.25, 95% CI (-0.32 to 0.87)

2st

t(146.56) = 1.41, p = 0.160, Cohen d = -0.45, 95% CI (-0.20 to 1.18)

pss_pa

1st

t(128.07) = 0.85, p = 0.397, Cohen d = -0.25, 95% CI (-1.10 to 2.75)

2st

t(145.34) = 1.24, p = 0.215, Cohen d = -0.41, 95% CI (-0.81 to 3.59)

pss_ps

1st

t(120.09) = -1.16, p = 0.250, Cohen d = 0.37, 95% CI (-4.83 to 1.27)

2st

t(141.49) = -1.89, p = 0.061, Cohen d = 0.68, 95% CI (-6.70 to 0.15)

pss

1st

t(117.45) = -1.13, p = 0.262, Cohen d = 0.38, 95% CI (-7.18 to 1.97)

2st

t(139.76) = -1.75, p = 0.082, Cohen d = 0.66, 95% CI (-9.66 to 0.58)

rki_responsible

1st

t(118.20) = 1.00, p = 0.320, Cohen d = -0.33, 95% CI (-0.82 to 2.50)

2st

t(140.28) = 0.43, p = 0.667, Cohen d = -0.16, 95% CI (-1.46 to 2.27)

rki_nonlinear

1st

t(119.30) = 0.79, p = 0.432, Cohen d = -0.26, 95% CI (-0.75 to 1.74)

2st

t(141.00) = 1.36, p = 0.178, Cohen d = -0.50, 95% CI (-0.44 to 2.35)

rki_peer

1st

t(123.67) = -0.22, p = 0.827, Cohen d = 0.07, 95% CI (-1.06 to 0.85)

2st

t(143.44) = 0.03, p = 0.976, Cohen d = -0.01, 95% CI (-1.06 to 1.10)

rki_expect

1st

t(140.36) = 2.09, p = 0.038, Cohen d = -0.53, 95% CI (0.02 to 0.87)

2st

t(148.66) = 2.20, p = 0.029, Cohen d = -0.65, 95% CI (0.06 to 1.04)

rki

1st

t(119.26) = 1.34, p = 0.184, Cohen d = -0.43, 95% CI (-0.81 to 4.16)

2st

t(140.98) = 1.36, p = 0.175, Cohen d = -0.50, 95% CI (-0.87 to 4.71)

raq_possible

1st

t(123.05) = -0.25, p = 0.802, Cohen d = 0.08, 95% CI (-0.84 to 0.65)

2st

t(143.13) = 1.37, p = 0.174, Cohen d = -0.48, 95% CI (-0.26 to 1.44)

raq_difficulty

1st

t(122.82) = -0.96, p = 0.341, Cohen d = 0.30, 95% CI (-0.88 to 0.31)

2st

t(143.02) = -0.02, p = 0.981, Cohen d = 0.01, 95% CI (-0.68 to 0.66)

raq

1st

t(118.02) = -0.61, p = 0.544, Cohen d = 0.20, 95% CI (-1.62 to 0.86)

2st

t(140.16) = 0.75, p = 0.452, Cohen d = -0.28, 95% CI (-0.86 to 1.92)

who

1st

t(112.64) = -0.04, p = 0.970, Cohen d = 0.01, 95% CI (-1.86 to 1.79)

2st

t(135.85) = 0.80, p = 0.424, Cohen d = -0.32, 95% CI (-1.20 to 2.83)

phq

1st

t(102.56) = 0.12, p = 0.903, Cohen d = -0.06, 95% CI (-1.37 to 1.55)

2st

t(123.09) = -0.10, p = 0.919, Cohen d = 0.05, 95% CI (-1.65 to 1.48)

gad

1st

t(112.42) = -0.21, p = 0.837, Cohen d = 0.07, 95% CI (-1.43 to 1.16)

2st

t(135.64) = -0.39, p = 0.694, Cohen d = 0.16, 95% CI (-1.72 to 1.15)

nb_pcs

1st

t(106.67) = -1.10, p = 0.275, Cohen d = 0.45, 95% CI (-4.83 to 1.39)

2st

t(129.18) = 0.15, p = 0.880, Cohen d = -0.07, 95% CI (-3.12 to 3.64)

nb_mcs

1st

t(118.96) = 0.36, p = 0.718, Cohen d = -0.12, 95% CI (-2.78 to 4.02)

2st

t(140.79) = 0.45, p = 0.653, Cohen d = -0.17, 95% CI (-2.95 to 4.69)

Within treatment group

sets

1st vs 2st

t(69.41) = -0.21, p = 0.838, Cohen d = 0.05, 95% CI (-0.88 to 0.71)

setv

1st vs 2st

t(67.03) = 0.54, p = 0.591, Cohen d = -0.13, 95% CI (-0.39 to 0.67)

maks

1st vs 2st

t(65.53) = -0.13, p = 0.900, Cohen d = 0.03, 95% CI (-1.11 to 0.98)

ibs

1st vs 2st

t(66.39) = 1.87, p = 0.065, Cohen d = -0.45, 95% CI (-0.04 to 1.20)

ers_e

1st vs 2st

t(66.02) = 0.93, p = 0.358, Cohen d = -0.22, 95% CI (-0.22 to 0.60)

ers_r

1st vs 2st

t(69.28) = 0.25, p = 0.800, Cohen d = -0.06, 95% CI (-0.46 to 0.59)

pss_pa

1st vs 2st

t(68.63) = -1.23, p = 0.222, Cohen d = 0.30, 95% CI (-2.60 to 0.62)

pss_ps

1st vs 2st

t(67.27) = -0.01, p = 0.995, Cohen d = 0.00, 95% CI (-2.32 to 2.30)

pss

1st vs 2st

t(66.84) = 0.63, p = 0.533, Cohen d = -0.15, 95% CI (-2.30 to 4.39)

rki_responsible

1st vs 2st

t(66.96) = -0.76, p = 0.448, Cohen d = 0.18, 95% CI (-1.70 to 0.76)

rki_nonlinear

1st vs 2st

t(67.14) = 0.41, p = 0.682, Cohen d = -0.10, 95% CI (-0.74 to 1.12)

rki_peer

1st vs 2st

t(67.87) = 0.58, p = 0.561, Cohen d = -0.14, 95% CI (-0.54 to 0.98)

rki_expect

1st vs 2st

t(71.09) = 0.93, p = 0.354, Cohen d = -0.22, 95% CI (-0.21 to 0.59)

rki

1st vs 2st

t(67.14) = 0.16, p = 0.873, Cohen d = -0.04, 95% CI (-1.71 to 2.01)

raq_possible

1st vs 2st

t(67.77) = 1.41, p = 0.163, Cohen d = -0.34, 95% CI (-0.17 to 1.01)

raq_difficulty

1st vs 2st

t(67.73) = 0.86, p = 0.391, Cohen d = -0.21, 95% CI (-0.26 to 0.67)

raq

1st vs 2st

t(66.93) = 1.35, p = 0.181, Cohen d = -0.33, 95% CI (-0.30 to 1.53)

who

1st vs 2st

t(66.06) = 0.91, p = 0.368, Cohen d = -0.22, 95% CI (-0.67 to 1.79)

phq

1st vs 2st

t(64.44) = 0.08, p = 0.935, Cohen d = -0.02, 95% CI (-0.75 to 0.81)

gad

1st vs 2st

t(66.02) = 0.34, p = 0.737, Cohen d = -0.08, 95% CI (-0.73 to 1.02)

nb_pcs

1st vs 2st

t(65.10) = 1.06, p = 0.293, Cohen d = -0.26, 95% CI (-0.87 to 2.85)

nb_mcs

1st vs 2st

t(67.09) = -0.06, p = 0.951, Cohen d = 0.01, 95% CI (-2.62 to 2.46)

Within control group

sets

1st vs 2st

t(77.49) = -0.76, p = 0.451, Cohen d = 0.18, 95% CI (-1.10 to 0.49)

setv

1st vs 2st

t(72.73) = 0.73, p = 0.471, Cohen d = -0.18, 95% CI (-0.34 to 0.73)

maks

1st vs 2st

t(69.58) = -0.18, p = 0.859, Cohen d = 0.04, 95% CI (-1.16 to 0.97)

ibs

1st vs 2st

t(71.39) = 0.43, p = 0.665, Cohen d = -0.11, 95% CI (-0.49 to 0.76)

ers_e

1st vs 2st

t(70.62) = -2.56, p = 0.013, Cohen d = 0.63, 95% CI (-0.95 to -0.12)

ers_r

1st vs 2st

t(77.23) = -0.58, p = 0.564, Cohen d = 0.14, 95% CI (-0.67 to 0.37)

pss_pa

1st vs 2st

t(75.96) = -1.92, p = 0.059, Cohen d = 0.46, 95% CI (-3.17 to 0.06)

pss_ps

1st vs 2st

t(73.22) = 1.27, p = 0.207, Cohen d = -0.31, 95% CI (-0.84 to 3.82)

pss

1st vs 2st

t(72.32) = 1.76, p = 0.083, Cohen d = -0.43, 95% CI (-0.39 to 6.36)

rki_responsible

1st vs 2st

t(72.58) = -0.06, p = 0.953, Cohen d = 0.01, 95% CI (-1.28 to 1.20)

rki_nonlinear

1st vs 2st

t(72.95) = -0.57, p = 0.569, Cohen d = 0.14, 95% CI (-1.21 to 0.67)

rki_peer

1st vs 2st

t(74.43) = 0.26, p = 0.794, Cohen d = -0.06, 95% CI (-0.66 to 0.86)

rki_expect

1st vs 2st

t(80.65) = 0.43, p = 0.669, Cohen d = -0.10, 95% CI (-0.31 to 0.48)

rki

1st vs 2st

t(72.94) = -0.10, p = 0.918, Cohen d = 0.03, 95% CI (-1.97 to 1.78)

raq_possible

1st vs 2st

t(74.22) = -0.89, p = 0.379, Cohen d = 0.22, 95% CI (-0.86 to 0.33)

raq_difficulty

1st vs 2st

t(74.14) = -0.32, p = 0.747, Cohen d = 0.08, 95% CI (-0.54 to 0.39)

raq

1st vs 2st

t(72.52) = -0.63, p = 0.529, Cohen d = 0.15, 95% CI (-1.21 to 0.63)

who

1st vs 2st

t(70.70) = -0.47, p = 0.641, Cohen d = 0.12, 95% CI (-1.54 to 0.95)

phq

1st vs 2st

t(67.26) = 0.51, p = 0.614, Cohen d = -0.13, 95% CI (-0.60 to 1.00)

gad

1st vs 2st

t(70.63) = 0.67, p = 0.503, Cohen d = -0.17, 95% CI (-0.59 to 1.19)

nb_pcs

1st vs 2st

t(68.68) = -1.05, p = 0.298, Cohen d = 0.26, 95% CI (-2.89 to 0.90)

nb_mcs

1st vs 2st

t(72.83) = -0.25, p = 0.801, Cohen d = 0.06, 95% CI (-2.89 to 2.24)

Plot